Again and Again: Is SN charachterized by Repetitive Music?

The chromagrams of last week suggested that a typical SN song is more repetitive than an atypical song (measured by average z-value of danceability, valence and energy features). To further inspect this hypothesis, I plotted self similarity matrices (SSM) for three atypical songs (z-value >|1.5|) and three typical songs (z-value <|.01|). The SSM’s plot timbre features for all songs, which were normalized via the Euclidean method. Although timbre features are not typically normalized, the Euclidean normalization yielded more pronounced visualizations. To summarize these features, a normal arithmetic mean was used and cosine distances were calculated to determine similarity.

As can be seen from the plots, the atypical songs seem to have more dynamic structures, shown by the complex checkered patterns that characterize these plots. However, the song the Farewell is less pronounced in this respect. In contrast, the typical songs overall seem to have fairly repetitive structures, though Why Don’t You is again an exception to this pattern.

All in all, there seems to be some support for the idea that SN is characterized by repetitive music but this is rather a tendency than a rule as the SSM’s show less of a stark difference than might be expected.

Music Taste over Time: the Story of a Playlist

The corpus that I have chosen is one of my personal playlists (songs: 406), one that I’ve started together with my girlfriend when we started dating and which we’ve been curating together ever since. Considering that we’ve been actively updating this playlist for years, I would like to know if our music taste has changed from when we started the playlist.

This portfolio tells the story of what I’ve found.

First, we need to be able to compare the old with the new. As such, I’ve split up SN into two parts: the first 100 songs added to the playlist (SN old) and the latest 100 additions (SN new). The graph on the left shows if there are meaningful differences between the means of a selection of Spotify audio features of SN old and SN new. Red lines indicate strong effects (> 0.5 standard deviations of change) whereas orange lines indicate medium effects (> 0.2 - < 0.5 standard deviations of change).

The graph seems to show that our music taste has become happier over time: danceability has gone up and valence has experienced a sharp rise. Only looking at a difference in means, however, tells us nothing of how the distributions of these features have evolved over time…

Happier Songs Over Time: The Great Migration of the Density Blob

On the left is a plot that shows how the distribution of the danceability and valence features has shifted over time. The first panel represents SN old (first 100 songs) and the last panel plots SN old (last 100 songs). To further show how the density of songs on these two dimensions has moved over time, I added a mid panel which I called SN mid, representing a 100 songs that were added in between SN old and new.

The strongest shift can be seen in the valence feature: whereas the majority of songs added in the beginning of the playlist are categorized as sad, with valences centering between 0.1-0.4, the latest songs added show virtually no focus at this region and include songs that explore the upper end of the continuum. Danceability too, has made a shift, albeit less strong than the valence feature.

What might have caused this shift in sound?

Forever Hipster: Were we Influenced by Newly Released Music?

One possibility as a cause for this shift in distribution is the influence of newly released music. Possibly the shift in playlist sound has less to do with a change in me and my girlfriend but rather has something to do with what is the latest and greatest music that’s being released at any given time.

The plot below shows that this is probably not the case. The date that songs were added are plotted against the time that they were released to see if there is a relationship between these two variables. To further underline this, I plotted a regression line which turned out almost completely flat. The plot further shows the distribution of the popularity of the tracks added over time, there seems to be a little bit of an overrepresentation of popular songs towards the latest additions but overall there seems to be a roughly equal distribution of popular/unpopular songs at any point in time.

Have we Found our True Sound?: Personal Means Convergence

It is my experience that collaborative playlists often have to find their stride. It takes some time to figure out what kind of sound a particular playlist should have and in its early days some additions of the participants can fail to line up in style. But after everyone has tested the water, often without explicitly agreeing about it with eachother, a sound is found and the styles converge.

Could it perhaps be that the difference between SN old and SN new is simply due to SN old encapsulating the unsure early days of the playlist and SN new representing the true sound of SN? The plot on the left offers some evidence for this hypothesis. It plots how the means of additions made by Sav or me, developed over time. The x-axis plots the course of time while the y-axis plots the mean value of both the danceability and valence features. As can be seen in the plot, SN old was characterized by some disagreement about where the playlist should go but after a little while we found our sound and settled on what the playlist should be.

This plot comes with a caveat: the means represented in SN old are based on much less data than the means of SN new, possibly exaggerating the differences. However, when plotting the mean differences based only on songs from SN new, there is still less of a discrepancy between the means compared to SN old.

Chromagram

On the left two chromagrams are plotted. These objects plot how sound energy is distributed over the classic Western 12 pitch classes at every given timepoint of a recording. A vertical slice of one of these chromagrams is a snapshot in time and shows how much or little energy is present for each note of the 12-note tonescale. These chromagrams were normalized by using the euclidean method.

The top chromagram plots a very typical SN song (Slowly by Max Sedgley), based on the features: danceability, valence and energy. Typicality, here, was determined by finding a song with a very small average z-value (<|.01|) for the aforementioned features. The same was done in reverse to find an atypical song: the below chromagram plots a song (the Rainbow by Talk Talk) with a high average z-value (>|1.5|) for these three features.

What stands out from these chromagrams is that Slowly is much more repetitive than the Rainbow. This makes sense because SN is mostly a playlist intended to enjoy when hanging out with friends and chilled more repetitive music is more suited for this than very dynamic tracks.